Abstract-Assessing car damage for insurance claims has traditionally been a slow and often inconsistent process. Typically, it entails manual inspections, which can be laborious, arbitrary, and susceptible to fraud or mistakes. Many insurers are increasingly using artificial intelligence, particularly deep learning and computer vision, to address these issues and make processes quicker and more dependable. Artificial intelligence (AI) systems can now examine images of damaged cars and determine the kind and degree of damage by utilising strong models like Convolutional Neural Networks (CNNs)., and even pinpoint its location all within seconds. This shift not only speeds up the claims process dramatically, but also reduces costs and removes a lot of guesswork from the equation. In this report, we lay out a full end-to-end solution: from collecting the right kind of image data, training and testing AI models, to integrating them into real insurance workflows. The results show major improvements in how fast and accurately claims are processed, and customers are noticing the difference. In short, AI isn’t just improving damage detection it’s reshaping the entire insurance claims experience to be quicker, more transparent, and fairer for everyone involved.
S,M , R,M and S,S . (2026). Revolutionizing Car Insurance Claims. (e736775). Mathematics and Computational Sciences, (), e736775 doi: 10.30511/mcs.2026.2077829.1627
MLA
S,M , , R,M , and S,S . "Revolutionizing Car Insurance Claims" .e736775 , Mathematics and Computational Sciences, , , 2026, e736775. doi: 10.30511/mcs.2026.2077829.1627
HARVARD
S M, R M, S S. (2026). 'Revolutionizing Car Insurance Claims', Mathematics and Computational Sciences, (), e736775. doi: 10.30511/mcs.2026.2077829.1627
CHICAGO
M S, M R and S S, "Revolutionizing Car Insurance Claims," Mathematics and Computational Sciences, (2026): e736775, doi: 10.30511/mcs.2026.2077829.1627
VANCOUVER
S M, R M, S S. Revolutionizing Car Insurance Claims. MCS. 2026;():e736775. doi: 10.30511/mcs.2026.2077829.1627
